Customer churn prediction has become a crucial task in the banking sector due to increasing competition and theneed for customer retention. This study presents a comprehensive approach for predicting customer churn usingMachine Learning (ML), Deep Learning (DL), and Quantum Machine Learning (QML) techniques. Traditionalstatistical approaches often fail to capture complex customer behavior patterns. Therefore, this work utilizesadvanced algorithms such as Logistic Regression, Random Forest, Gradient Boosting, Artificial Neural Networks,and Variational Quantum Classifiers.The proposed system processes customer demographic, transactional, and behavioral data to predict churnprobability. Experimental results demonstrate that ensemble-based ML models and deep learning techniquesprovide high predictive accuracy, while QML offers a future-oriented exploratory approach. The system enablesbanks to identify high-risk customers and implement targeted retention strategies, thereby improving profitabilityand customer satisfaction